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cbd compression

First of all, I would like to extend my thanks to Dr. Hsiao and co-authors for the interesting report of an unusual case, ‘Obstructive jaundice as a complication of a right hepatic artery pseudoaneurysm after laparoscopic cholecystectomy’.[1] I read the article with enthusiasm but got a bit confused. Therefore I would like to present two comments.

Second comment: This involves the pathophysiology of obstructive jaundice. Starting with the abstract, I realised that the condition the patient had was haemobilia because of the clinical presentation, that is, tarry stools and obstructive jaundice. But later in the discussion it is stated that endoscopy was not performed because of the absence of gastrointestinal bleeding. So the key question is: What is the pathogenesis of obstructive jaundice, common bile duct (CBD) compression or Quincke’s triad? If the patient had haemobilia, the clinical presentation of haemobilia is Quincke’s triad (biliary colic, melena and obstructive jaundice). Of these elements, obstructive jaundice is seen in 60% of all haemobilia cases, biliary colic in 70% and melena in 90%.[3] The pathophysiological mechanism of jaundice in haemobilia is CBD obstruction by blood clots formed in pseudo-aneurysmal sac and transferred to CBD via fistula. Thus, CBD compression is not mandatory for the development of obstructive jaundice in a patient with haemobilia. Regarding imaging, air bubbles, blood clots and metallic clips in CBD can also lead to false positive diagnoses of choledocholithiasis or CBD obstruction.

First comment: The authors stated that obstructive jaundice due to hepatic artery pseudoaneurysm had never been reported before. However, Peter et al. published a case of obstructive jaundice in a patient with severe haemobilia due to common hepatic artery pseudoaneurysm (5.7 × 5.3 cm) in 2014.[2]

Cbd compression

CBD provides a one-shot method for determining the level of similarities between two microbiotas. CBD omits the need for expert interventions in assigning similar sequences to OTUs as well as aligning sequence reads, generating phylogenetic trees, realigning sequence reads, and choosing proper software and parameters. For comparison purposes, we used the microbiota analysis toolboxes mothur and QIIME which have implemented automated to semi-automated functions for microbiota comparisons such as UniFrac (Table 1) [40, 41].

Results

The development of advanced and cost-effective DNA sequencing techniques enables the generation of tremendous datasets. For example, three recent studies reported that Illumina GAIIx or HiSeq platform produced millions of reads [45-47]. To accommodate this high-throughput data generation, simple and fast tools are extremely important for efficiently and accurately extracting information to further characterize microbiota. Increasing the efficiency of microbial community comparisons has profound implications for research. The CBD method described here facilitates efficient similarity comparisons between microbiotas.

Conclusion

Perturbations in intestinal microbiota composition have been associated with a variety of gastrointestinal tract-related diseases. The alleviation of symptoms has been achieved using treatments that alter the gastrointestinal tract microbiota toward that of healthy individuals. Identifying differences in microbiota composition through the use of 16S rRNA gene hypervariable tag sequencing has profound health implications. Current computational methods for comparing microbial communities are usually based on multiple alignments and phylogenetic inference, making them time consuming and requiring exceptional expertise and computational resources. As sequencing data rapidly grows in size, simpler analysis methods are needed to meet the growing computational burdens of microbiota comparisons. Thus, we have developed a simple, rapid, and accurate method, independent of multiple alignments and phylogenetic inference, to support microbiota comparisons.